Germs, Social Networks and Growth

Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, how does this effect operate and how big is it? Using network analysis tools, we explore how different social structures affect technology diffusion and thereby a country’s rate of technological progress. The network model also explains why societies with a high prevalence of contagious disease might evolve toward growth-inhibiting social institutions and how small initial differences can produce large divergence in incomes. Empirical work uses differences in the prevalence of diseases spread by human contact and the prevalence of other diseases as an instrument to identify an effect of social structure on technology diffusion. How does the pattern of social connections between individuals affect a country’s income? Macroeconomists typically overlook findings of sociologists and anthropologists because social characteristics are difficult to observe, to describe formally and to quantify.1 This paper uses tools from network analysis to explore how different social structures might affect a country’s rate of technological progress. The network model also explains why societies might adopt growth-inhibiting structures and allows us to quantify the potential size of these effects. Motivated by the model, we use differences in the prevalence of diseases spread by human contact and the prevalence of other diseases as an instrument to identify an effect of social structure on technology diffusion. There is a long history of measuring the speed of information or technology diffusion within various kinds of networks (Jackson (2008), Granovetter (2005)). Given these findings, a simple ∗Corresponding author: [email protected], Department of Economics, University of Minnesota, 90 Hennepin Ave. Minneapolis, MN 55405. [email protected], 44 West Fourth St., rm 7-77, New York, NY 10012. We thank participants at the Minnesota Workshop in Macroeconomic Theory, NBER EF&G meetings, SED meetings, the Conference on the Economics of Interactions and Culture and Einaudi Institute, the Munich conference on Cultural Change and Economic Growth, SITE, NBER Macro across Time and Space, and NBER growth meetings and seminar participants at Bocconi, Brown, USC, Stanford, Chicago, Western Ontario, Minnesota, Penn State, George Washington, and NYU for their comments and suggestions. We thank Corey Fincher and Damian Murray for help with the pathogen data, Diego Comin, Pascaline Dupas, Chad Jones, and Marti Mestieri, for useful comments, and Isaac Baley, David Low, and Amanda Michaud for invaluable research assistance. Laura Veldkamp thanks the Hoover Institution for their hospitality and financial support through the national fellows program.